Types Of Questions Research Articles

Shifting the spotlight: What do we mean by ‘religious language’?

Abstract There are mainly two types of questions asked about religious language: those about identity (e.g., what is a religious language?) and those about meaning (e.g., what do its sentences say?). Most philosophers focus on the latter because while they disagree about meaning, they agree that some sentences are religious and that our understanding of them does not depend on us knowing what makes them religious. In this article, I provide two reasons why questions about identity should receive more attention. First, theories of identity and theories of meaning share a two-way relationship where the characteristics of one influence those of the other, and so overlooking identity overlooks important characteristics of meaning. Second, the study of religious language has been shaped by this relationship for some time and being aware of it improves our understanding of conventional trends and contemporary debates. If successful, this article will motivate philosophers to reconsider the role of identity in research and to dedicate more effort to its study.

A guideline-informed language model for paediatric cardiology demonstrates high performance in answering complex medical questions

Abstract Background Paediatric cardiology presents unique challenges with its diverse and complex cases, limited evidence base, and the necessity for multi-expert involvement in decision-making processes. In this context, the introduction of generative pre-trained transformer (GPT) based large language models (LLMs) offers a potential avenue for the provision of complex information and clinical decision support. Purpose This study evaluates the quality of three different GPT LLMs in answering complex medical questions, including a state-of-the-art preview model that incorporates the German paediatric cardiology guidelines. Methods Seven paediatric cardiologists and paediatric cardiac surgeons generated 72 questions, including complex questions and medical cases with associated questions. The questions were categorized by difficulty and required knowledge (factual and experience-based or mostly experience-based). We prompted the questions to three LLMs: GPT 3.5, GPT 4 and a GPT 4 turbo preview. The GPT 4 turbo preview was customized by incorporating all guidelines from the German Society for Paediatric Cardiology by a retrieval function. Employing one complex instruction for all questions, we prompted the LLMs to provide precise and detailed expert-level responses. The responses from each model were evaluated by experts based on relevance, factual accuracy, severity of possible harm, completeness, superfluous content, and age-related appropriateness from 0 (very bad) to 7 (very good). Differences were calculated using the Kruskal-Wallis-test in SPSS Version 28. Results Our findings indicated a good performance of all models regarding the dimensions tested. The figures show the average ratings (Figure 1, Figure 2A) and highlight significant differences after Bonferroni correction in bold (Figure 2B). The GPT 4 turbo preview, including the retrieval of guidelines, provided significantly more relevant (average rating [AR] 5.94, meaning mostly relevant), accurate (AR 5.6, meaning between somewhat and mostly accurate) and complete (AR 5, meaning fairly complete) answers compared to GPT 3.5 and GPT 4. In terms of difficulty levels or the type of questions, there was no significant difference in rating. Relevance ratings were slightly better in factual questions (AR 5.7) than in those requiring more experience-based knowledge (AR 5,3). Although GPT4 had higher average scores compared to GPT 3.5 in all dimensions except superfluous content, the differences in rating were not statistically significant. All models had relevant difficulties considering the age-related aspects of the questions (AR 4.06-4.45, p=0.455). Conclusion This study highlights the potential and limitations of AI language models in addressing complex medical questions in fields characterized by highly individualized decision-making scenarios. The findings advocate for the development of more specialized AI tools in medicine, tailored to specific medical fields and patient age groups.Fig 1:Average ratings of LLMsFig 2:Rating differences between LLMs

Technology-assisted instruction with teacher prompts on fraction multiplication word problems: A single-case design with visual analysis and Bayesian multilevel modeling

ABSTRACT This study examines the effects of technology-assisted instruction with teacher prompts on the ability to visualize and solve fraction multiplication word problems for four middle school students with learning disabilities. A multiple-probe design across participants, a type of single-case design, showed a functional relationship between the intervention and the targeted mathematical outcomes. Although there were some fluctuations over time, students demonstrated improvements from the baseline to intervention phases (Tau ranged from 0.76 to 1.00 for visualization and was 1.00 for problem-solving). Researchers employed Bayesian cumulative link mixed effects models to examine the moderating effects of word problem question types. Students showed greater maintenance effects on problem-solving than on visualization tasks, as reflected in changes in level (logit coefficient = 2.6) and trend (logit coefficient = 0.22). Students and teachers perceived vocabulary and multiplication fact practices, as well as the cognitive and metacognitive features embedded in the technology-assisted intervention, to be useful in learning targeted mathematics concepts. This study underscores the role of technology integration in enhancing teachers’ instructional approach and in aiding students’ acquisition and retention of mathematical concepts and skills.

A comparison of drug information question responses by a drug information center and by ChatGPT.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. A study was conducted to assess the accuracy and ability of Chat Generative Pre-trained Transformer (ChatGPT) to systematically respond to drug information inquiries relative to responses of a drug information center (DIC). Ten drug information questions answered by the DIC in 2022 or 2023 were selected for analysis. Three pharmacists created new ChatGPT accounts and submitted each question to ChatGPT at the same time. Each question was submitted twice to identify consistency in responses. Two days later, the same process was conducted by a fourth pharmacist. Phase 1 of data analysis consisted of a drug information pharmacist assessing all 84 ChatGPT responses for accuracy relative to the DIC responses. In phase 2, 10 ChatGPT responses were selected to be assessed by 3 blinded reviewers. Reviewers utilized an 8-question predetermined rubric to evaluate the ChatGPT and DIC responses. When comparing the ChatGPT responses (n = 84) to the DIC responses, ChatGPT had an overall accuracy rate of 50%. Accuracy across the different question types varied. In regards to the overall blinded score, ChatGPT responses scored higher than the responses by the DIC according to the rubric (overall scores of 67.5% and 55.0%, respectively). The DIC responses scored higher in the categories of references mentioned and references identified. Responses generated by ChatGPT have been found to be better than those created by a DIC in clarity and readability; however, the accuracy of ChatGPT responses was lacking. ChatGPT responses to drug information questions would need to be carefully reviewed for accuracy and completeness.

Impact of modality and mode of questioning and testing on memory reports

IntroductionIndividuals' memories are assessed in multiple contexts; however, depending on the context, how an individual is questioned may impact the quantity and quality of the details reported. One goal of this study is to investigate how the modality of questioning (individuals talk or write about an event) impacts memory reports. Additionally, being tested on previously learned information improves memory for that information compared to re-studying it. Consequently, another goal is to examine how questioning impacts memory reports compared to a second exposure. We utilized open-ended and pointed questions (true and false).MethodParticipants watched a short video and were questioned (Experiment 1: In-Person; Experiment 2: Virtual) about its contents immediately, 1 week, and 1 month later.ResultsThe current study found that writing leads to better quality memory reports than speaking, and the benefit is present 1 week later. Additionally, we found that writing mitigates an anticipated testing benefit, although this depended on whether a pointed or open-ended question was asked. Restudying (vs. immediate testing) led to better performance for the false pointed questions. However, the better performance operated differently depending on whether participants wrote or spoke following restudying, perhaps due to a differential criterion shift between the Restudy-Written and the Restudy-Spoken conditions.DiscussionWe conclude that the impact of the modality of questioning is influenced in several ways by the types of questions asked, which bears significance for many domains because one modality (or a combination) may be more suitable for producing more accurate memory reports as a function of different domains.

Development of the Web Application to Support the Process of Conducting Intellectual Games

The article discusses technologies for developing the web application designed to accompany the process of conducting an intellectual game in the format of an interactive quiz. An analysis of similar applications was carried out, which showed that none of them satisfies all the criteria essential in this situation. Requirements for the web application have been determined: the ability to create a library of quizzes, various types of questions, edit ready-made quizzes, participants enter answers through their devices online, award points for the correctness and speed of answers, display questions on the presenter's screen and on team devices, view intermediate and final results in the form of a list, returning participants to the game if necessary, saving the points received. The process of developing an optimized user interface using HTML, CSS, JavaScript, which allows users to interact with the web application, is described, the structure of the web application, the main forms of the administrator and game participants are presented. The server side of the application was developed using Node.JS technologies, including the Socket.io library, and the MongoDB database, the resulting collection of non-relational database is shown. To implement the functionality of the application pages, JavaScript was used; examples of codes are presented that implement the mechanism for connecting the presenter to the game, retrieving information from the database, recording the team's response, and awarding points for the correctness and speed of the response. The web application allows you to reduce the time spent by event organizers and participants and makes it possible to improve the organization of work in general. The presented web application for accompanying intellectual games was successfully approbated during events at the Nizhny Tagil State Social-Pedagogical Institute (branch) of the Russian State Vocational Pedagogical University. The materials of the article can serve as practical recommendations for the development of such web applications to accompany intellectual games held in the format of interactive quizzes.

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors.

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of to , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

Analysis Of The Marketing Mix And Its Influence On Purchasing Decisions On The Prasetya Products

This study aims to identify, study and analyze the marketing mix of The Prasetya and The Influences in Customer Decisions to by product of The Prasetya. This study used qualitative research methods, with data collection techniques observation, interviews and explore theh types of open-ended questions where information is free to provide in depth information to be used as research data. The data analyzed is the result of interviews with employees, customers and experienced human resourch of UMKM using analyctical techniques including data reduction, data presentation and levers or cunclusions. The result of this study that marketing mix of The Prasetya has been able to influence consumer purchasing decisions and increase sales turnover and customers always make repeat purchases for the product and also increase the company's profits so that the company's goals can be achieved.

How We Communicate Forgiveness to Our Partners Matters: Forgiveness-Granting Strategies, Forgiveness Tendency, Commitment, and Relationship Satisfaction in Dating and Married Couples

Abstract Individuals in dating and marital relationships experience various transgressions that require forgiveness (e.g., hurtful communication, mistrust, lies, or infidelity). This study examines forgiveness-granting strategies in the context of forgiveness tendency, relationship length, commitment, satisfaction, and relational consequences to better understand relationship dynamics. 649 individuals (532 dating, 117 married), aged 18–65 (M = 26.00, SD = 8.82) participated. The sample included 517 women (79.7 %) and 132 men (20.3 %). Participants described a forgiven transgression and completed questionnaires, including the Multidimensional Forgiveness-Granting Scale (MFGS), Transgression-Related Interpersonal Motivations Inventory (TRIM-18), Relationship Assessment Scale (RAS), and the commitment subscale from the Investment Model Scale. One open-ended question (types of forgiven transgressions) was analyzed consensually. Quantitative data were analyzed using correlational analysis, regression analysis, and t-tests. The most common transgression was infidelity. A linear regression analysis showed that the strongest positive predictor of relationship satisfaction was the tendency to forgive one’s partner. Nonverbal displays positively predicted relationship strengthening, while the discussion strategy predicted relationship deterioration negatively. These findings emphasize the importance of how forgiveness is communicated and may inform interventions to enhance romantic relationships.

Types of Questions Asked by Teachers at Primary School 077786 Tuhembaruzo in Classroom Interaction

This study aims to analyze the types of teacher questions in the teaching and learning process in three study groups and to analyze student activeness in the learning process at SD Negeri 077786 Tuhembaruzo. To obtain the results of this research, a descriptive qualitative method was used because this research focuses on phenomena that occur in the classroom. After analyzing it was found that there were 3 types of questions used by teachers in the study groups, namely procedural, convergent and divergent types of questions. the type of teacher questions in the three study groups at SD Negeri 077786 Tuhembaruzo was more convergent type than divergent and procedural types, namely 57, 23% of questions. While the teacher's procedural type questions amounted to 19.07% and the divergent type amounted to 23.68%.

Automated Test Creation Using Large Language Models: A Practical Application

The article presents work on developing a software application for test creation using artificial intelligence and large language models. Its main goal is to optimize the educators’ work by automating the process of test generation and evaluation, with the tests being stored for subsequent analysis and use. The application can generate test questions based on specified criteria such as difficulty level, Bloom’s taxonomy level, question type, style and format, feedback inclusion, and more, thereby providing opportunities to enhance the adaptability and efficiency of the learning process. It is developed on the Google Firebase platform, utilizing the ChatGPT API, and also incorporates cloud computing to ensure scalability and data reliability.

The Role of Language in Remote Healthcare Triage: AMeta-Aggregative Review.

To provide an overview of evidence on the role of language in remote healthcare services prioritisation, from now on termed triage. This study synthesises literature, to better understand how language affects triage interactions, aiming to improve these processes. We conducted a meta-aggregative review. A systematic search in Scopus from inception to September 2023 identified 437 studies on language in remote healthcare triage, of which 23 were included. Information was selected using both inductive and deductive coding, focusing on six linguistic features of interaction that have been described in the literature on studies using conversation analysis: turn-taking organisation, overall structure, sequence organisation, turn design, lexical choice, and epistemological and other forms of asymmetry. The process followed the RAMESES Publication Standards for Meta-narrative Reviews. Two main findings emerged. First, all six linguistic features are present in triage conversations, indicating that language involves more than just what is said. It also matters, for example, how and when a question is asked. Second, computerised decision support systems (CDSS) significantly affect conversation flow and dynamics. Language in triage involves more than literal speech and is heavily influenced by CDSS. Our study suggests that quality assessments of triage conversations should consider not only what is said but cover all relevant aspects of language. The influence of computerised decision support systems (CDSS) on linguistic features highlights the need for systems to be adaptable, to improve conversation quality and better addressing caller needs rather than focusing on one-size-fits-all questions. This review highlights the complex role of language in triage conversations and its impact on interaction. It calls for a broader view of language in quality assessments, recognising that both call-takers and callers contribute to call quality. Insights from this review can help developers enhance question types, sequence, and delivery methods of computerised decision support systems. Finally, education for call-takers in healthcare sectors may be improved based on our findings. No patient or public contribution.

Did Reagan Decline?

When Ronald Reagan revealed his Alzheimer's diagnosis in 1994, it rekindled a lingering question: did dementia affect Reagan during his eight years as president? Amid countless countervailing anecdotes, Berisha et al. (2015) stepped in with an inventive systematic test. Scouring Ronald Reagan's 46 formal press conferences for specific linguistic markers, the study discovered "significant differences in variables known to be associated with the onset of dementia" (962). Here I test whether Reagan's unique word usage rate decline is spurious, a function of reporters' increasing penchant for asking "follow-up" questions. Focusing on the President's specific responses to distinct questions, I reanalyze Reagan's unique word usage rate while holding constant the number and type of reporter questions. I find Reagan's unique word usage rate held form throughout his eight years in the White House. I conclude by considering the implications for Reagan's legacy and Alzheimer's research.

Using ChatGPT to Provide Patient-Specific Answers to Parental Questions in the PICU.

To determine if ChatGPT can incorporate patient-specific information to provide high-quality answers to parental questions in the PICU. We hypothesized that ChatGPT would generate high-quality, patient-specific responses. In this cross-sectional study, we generated assessments and plans for 3 PICU patients with respiratory failure, septic shock, and status epilepticus and paired them with 8 typical parental questions. We prompted ChatGPT with instructions, an assessment and plan, and 1 question. Six PICU physicians evaluated the responses for accuracy (1-6), completeness (yes/no), empathy (1-6), and understandability (Patient Education Materials Assessment Tool, PEMAT, 0% to 100%; Flesch-Kincaid grade level). We compared answer quality among scenarios and question types using the Kruskal-Wallis and Fischer's exact tests. We used percent agreement, Cohen's Kappa, and Gwet's agreement coefficient to estimate inter-rater reliability. All answers incorporated patient details, utilizing them for reasoning in 59% of sentences. Responses had high accuracy (median 5.0, [interquartile range (IQR), 4.0-6.0]), empathy (median 5.0, [IQR, 5.0-6.0]), completeness (97% of all questions), and understandability (PEMAT % median 100, [IQR, 87.5-100]; Flesch-Kincaid level 8.7). Only 4/144 reviewer scores were <4/6 in accuracy, and no response was deemed likely to cause harm. There was no difference in accuracy, completeness, empathy, or understandability among scenarios or question types. We found fair, substantial, and almost perfect agreement among reviewers for accuracy, empathy, and understandability, respectively. ChatGPT used patient-specific information to provide high-quality answers to parental questions in PICU clinical scenarios.

Engagement Strategies in a Peer-quizzing Game: Investigating Student Interactions and Powergaming

Educational games are a popular means of engaging students in learning activities. However, students in game-based learning environments often engage in powergaming to reap undeserved rewards and spoil the experi-ence of their peers. This may happen when the students pretend to engage in game activities or collude with other students, exploiting the game settings and rules to maximize their points. In this explorative study, we investigate powergaming in three settings of an asynchronous online multiplayer peer-quizzing game in a blended learning setting – a first-year programming class in a Canadian university. We designed three experi-mental settings with three versions of an education game which allowed different levels of powergaming. The between-subject study involved three experimental groups: Group 1 used a game version where they received weekly performance feedback and tips on how to improve their performance, Group 2 used a game version with access to an existing resource bank, allowing them to maximize the number if their activities in the game, and Group 3 served as a control group with no additional interventions. The research aimed to investigate 1) the association between the power gamers' activity, their grades and their preference to work alone or in a group, 2) the types of activities (type of quiz questions) most used by power gamers, and 3) which game setting was the most conducive to power gaming. The results show that better grades are not associated with higher activity levels in the game. Students who engaged in more diverse types of game activities had better learning outcomes The control Group 3 had the highest average grades. As we expected, Group 2 engaged in the high-est number of activities, esp. in creating questions, a form of powergaming. The qualitative results showed that contrary to our assumption the powergamers in Group 2 was not harmful, because it created a rich set of re-sources in the game and fostered student engagement in the game.

Quantitatively Measuring Developmental Characteristics in the Use of Deictic Verbs for Japanese-Speaking Children: A Pilot Study

The acquisition of deictic verbs is a significant milestone in language development. This complex process requires an understanding of the interplay between the personal pronouns “I/you” and deictic verbs. Although demonstrating the cognitive processes associated with deictic shifting through data is valuable, research issues regarding data accuracy and the spatial arrangement of the self and other remain unresolved. This pilot study aimed to quantitatively measure the body movements of Japanese-speaking children during their utterances of “come/go”. Twelve typically developing children aged 6–7 participated in this study. Multiple scenarios were set up where the researcher presented phrases using “come/go” with deictic gestures, such as moving one’s upper body forward or backward, and the participant replied with “come/go”. When performing a role, the researcher sat face-to-face or side-by-side with the participant, depending on the type of question–response. It is possible that there is a learning process whereby verbal responses using “come/go” align with corresponding body movements in the specific question type. This process is deeply involved in the development of perspective-taking abilities. Future research with relatively large samples and cross-cultural comparisons is warranted to deepen the understanding of this linguistic acquisition process and its implications.

Benefits and Drawbacks of International Medical Elective Databases in Abroad Elective Research: A Narrative Review.

International medical electives (IMEs) are considered high-impact practice in global health education. Nevertheless, international medical elective (IME) research remains scarce, with only a few new publications appearing each year. The discrepancy between the many unanswered questions regarding IMEs and the lack of opportunities to perform research in this field has been further aggravated by the COVID-19 pandemic. Elective databases cataloguing structured IME reports/testimonies could offer a viable solution here. This narrative review provides a balanced and objective evaluation of the strengths and weaknesses of elective databases, summarizing their potential usefulness in IME research. The methodology employed was a multidisciplinary narrative review of the published and grey literature on databases cataloguing IME testimonies. Elective databases offer numerous benefits to the IME researcher. Their size allows for large analyses, built on hundreds of equally structured elective testimonies. Pre-defined outcomes, such as the elective destination, elective discipline or duration, are queried in a standardized way, allowing for a broad set of research questions. Elective databases are usually open-access, not confined to a single university, and free to use. Most databases also offer user-friendly filter functions, permitting targeted analyses centered around a particular outcome. A major drawback is that reports are rarely verified. Subject to several forms of bias (eg, recall and reporting bias), elective databases may not be suitable for all types of research questions, and the report quality is often inhomogeneous. Above all, they rarely allow for an informational depth that may result from qualitative face-to-face interviews. Elective databases could be a valuable supplement to interview-based elective research, potentially allowing for larger and broader analyses not confined to single institutions.

The FDA Sentinel Real World Evidence Data Enterprise (RWE-DE).

The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.

PECULARITIES OF DEVELOPING CRITICAL READING ABILITY OF THE MASTERS OF THE SPECIALTITY 7М01712 – TRAINING OF FOREIGN LANGUAGE TEACHERS

The article reveals the development of critical reading skills of master's students of the specialty 7M01712 - ‘Preparation of teachers of foreign languages’. The article emphasizes the importance of using lecture texts in the educational process to prepare master's students for professional activities. Effective use of professional texts in the classroom contributes to the improvement of scientific and professional speech and communicative competence of master's students, which in turn provides the optimal use of various methods of critical analysis in their future professional activities. Special attention is paid to the development of independent thinking skills, search, analysis, systematization, structuring and communication of information.The author identifies three stages of critical reading development. The author also suggests types of questions that promote the development of this type of reading, as well as the ability to identify contradictions and types of structures present in a text and to argue one's point of view based not only on logic, but also on the ideas of the interlocutor. The use of various methods and tools of critical reading technology at the stage of challenge, at the stage of reflection and at the stage of analysis is described. Methodological recommendations on mastering the technology of critical reading are intended for teachers of a foreign language, but can be useful for teachers of other subjects, since the technology of critical reading development belongs to the interdisciplinary modern educational technologies. The article presents practical experience of using some techniques of critical reading technology on the example of the special course “Cognitive linguocultural methodological foundations”.

Integrating an incident dataset with a question and answering language model to assist hazard identification: Comparison of an extractive and generative model

Robust hazard identification (HAZID) relies upon extensive knowledge of the system being analysed, the technical aspects, and how it will be used operationally. Typically, this knowledge is held by human participants who can draw out answers in natural language to hazard related questions based upon their own experience. However, several threats exist to this, such as high staff turnover, a poor learning from incidents capability or even insufficient Information Technology resources. Alternatively, incident databases hold vast amounts of hazard information that can be transformed into a source of knowledge. As mitigation to the aforementioned issues, this paper presents a Question and Answering (Q&amp;A) Bidirectional Encoder Representations from Transformers (BERT) language model trained upon aviation incidents and a unique Q&amp;A dataset. The model can extract answers to typical HAZID questions, based upon factual incident reports. Alongside this extractive approach, the paper also explores the use of a generative Large Language Model combined with an incident dataset. Both models proved a useful addition to HAZID activities based upon the Structured What If Technique (SWIFT), answering safety-themed questions based upon a retrieved context of incident reports that semantically matched the query. For the purposes of HAZID, it was suggested that the generative option is preferable based upon its ease of implementation, lower resource requirements and quality of responses. Additionally, it is shown that it is possible for organisations to train and create their own custom models for HAZID purposes. Future work may wish to consider the application of models that can hypothesize scenarios based upon incident reports, building further understanding to the relationships between causes, hazards and consequences.